A variable gain PI to improve the performances of a unified power flow controller system

The instability problems in the electrical supply networks have had a great impact on recent research studies on modern devices. The unified power flow controller (UPFC) is one of the various FACTS (Flexible Alternative Current Transmission Sys-tems) devices that allow the electrical supply networks to be stable with a strong effectiveness. In this paper, the performances of such a device using both a classical PI and a variable gains PI controllers are examined. For this instance, the compensator is first stabilized before trying to stabilize the network. A series of comparative simulation tests have been undertaken for both regulators and analyzed. From the obtained results it is clearly shown that when the system is equipped with the variable gain PI regulator, the performance are much better

ANN-Based Failure Modeling of T-56 Engine Turbines

The T-56 turboprop engine is one of the most widely used in military transportation aircraft. It operates virtually everywhere, from the arctic circle to the Sahara. Operation in desert conditions, however, presents a challenge for maintenance engineers regarding preventive maintenance scheduling. Erosion caused by sand particles drastically decreases turbine blades life. Recent studies showed that Artificial Neural Network ANN algorithms have much better capability at modeling reliability and predicting failure than conventional algorithms. In this study, more than thirty years of local operational field data were used for failure rate prediction and validation using several algorithms. These include Weibull regression modeling to establish a reference, feed-forward back-propagation ANN, and radial basis neural network algorithm. Comparison between the three methods is carried out. Results show that the failure rate predicted by both the feed-forward back-propagation artificial neural network model and radial basis neural network model are closer to actual failure data than the failure rate predicted by the Weibull model. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer

Simulation of Transonic Compressor Performance Deterioration Due to Sand Erosion

Air sand erosion is a widely effecting phenomenon in GCC region, where a solid particle impacting on a wall surface causing catastrophic mechanical damage, the engine compressor operating in a particulate environment are subjected to deterioration of performance and life due to sand ingestion. In current paper we simulate and studied the sand erosion in such transonic compressors were flow rate, particle size and concentration were investigated to study particles distribution and erosion rate along the compressor blade. Result shows that particles concentration has the most significant effect on blade erosion rate where particles size has less effect among all other measured parameters. Surface deformation and roughness is in scope for further investigation

Glueball Production in Peripheral Heavy-Ion Collisions

The method of equivalent quanta is applied both to photon-photon and, by analogy, to double pomeron exchange in heavy-ion collisions. This Weizs\"acker-Williams approach is used to calculate production cross sections for the glueball candidate $f_J(1710)$ meson via photon-photon and pomeron-pomeron fusion in peripheral heavy-ion collisions at both RHIC and LHC energies. The impact-parameter dependence for total and elastic cross sections are presented, and are compared to results for proton-proton collisions.Comment: 15 pages, 6 figure

Glueball plus Pion Production in Photon-Photon Collisions.

We here compute the reaction $\gamma \; \gamma \rightarrow G \; \pi^{0}$ for various glueball candidates $G$ and their assumed quantum states, using a non-relativistic gluon bound-state model for the glueball.Comment: To appear in Zeit. fur Phys. C; Plain Latex file, 16 pages; 5 figures appended as a uuencoded postscript file

FACADE RECONSTRUCTION FOR TEXTURED LOD2 CITYGML MODELS BASED ON DEEP LEARNING AND MIXED INTEGER LINEAR PROGRAMMING

The paper describes a workflow for generating LoD3 CityGML models (i.e. semantic building models with structured facades) based on textured LoD2 CityGML models by adding window and door objects. For each wall texture, bounding boxes of windows and doors are detected using “Faster R-CNN”, a deep neural network. We evaluate results for textures with different resolutions on the ICG Graz50 facade dataset. In general, detected bounding boxes match very well with the rectangular shape of most wall openings. Thus, no further classification of shapes is required. Windows are typically aligned to rows and columns, and only a few different types of windows exist for each facade. However, the neural network proposes rectangles of varying sizes, which are not always aligned perfectly. Thus, we use post-processing to obtain a more realistic appearance of facades. Window and door rectangles get aligned by solving a mixed integer linear optimization problem, which automatically leads to a clustering of these openings into few different classes of window and door types. Furthermore, an a-priori knowledge about the number of clusters is not required

An ultra-thin diamond membrane as a transmission particle detector and vacuum window for external microbeams

Several applications of external microbeam techniques demand a very accurate and controlled dose delivery. To satisfy these requirements when post-sample ion detection is not feasible, we constructed a transmission single-ion detector based on an ultra-thin diamond membrane. The negligible intrinsic noise provides an excellent signal-to-noise ratio and enables a hit-detection efficiency of close to 100%, even for energetic protons, while the small thickness of the membrane limits beam spreading. Moreover, because of the superb mechanical stiffness of diamond, this membrane can simultaneously serve as a vacuum window and allow the extraction of an ion microbeam into the atmosphere

A SWEEP-PLANE ALGORITHM FOR THE SIMPLIFICATION OF 3D BUILDING MODELS IN THE APPLICATION SCENARIO OF WIND SIMULATIONS

As the number of virtual 3D city models is steadily increasing, so are the possible applications that take advantage of them. 3D models can be used for applications that range from simple graphic visualizations to complex simulations, such as air flow and acoustic simulations. The geometric requirements needed for Computer Aided Engineering (CAE) and Computational Fluid Dynamics (CFD) increase the already very high complexity of processing 3D models. If there are too many small geometric details, mesh generation may fail. In addition it will create small grid cells that consequently lead to a high computation time. So far, the necessary simplifications have been performed in a time consuming manual process. To reduce the preprocessing time for the considered simulation topic, the simplifications and modifications have to be automated. In this paper we introduce a sweep-plane algorithm designed to automatically simplify virtual 3D models (e.g. CityGML) by removing geometry information unnecessary for numerical simulations. The algorithm will search for edges whose length does not reach a predefined threshold and dissolve them by sweeping nearby faces. As a result we obtain a simplified geometry that can be meshed properly. This algorithm serves as a general basis for the creation of future simplification algorithms that may even be applicable to any simulation necessary. For this paper, one of Stuttgart’s city blocks was processed with the developed algorithm and then used in a wind simulation carried out with ANSYS Fluent